StarCoder Statistics And User Trends 2026

StarCoder2-3B recorded 163,000 monthly downloads as of 2025, making it the most popular variant in the open-source code generation platform developed by the BigCode Project. The AI coding tools market reached $7.37 billion in 2025 and projects to hit $23.97 billion by 2030. StarCoder2-15B-Instruct achieved a 72.6% score on HumanEval benchmarks, marking the first fully transparent code model to exceed 70% on this standard.

StarCoder Key Statistics

• StarCoder2-3B has 163,000 monthly downloads as of 2025, representing the highest adoption among all variants
• The AI coding tools market reached $7.37 billion in 2025 with a projected 26.60% compound annual growth rate through 2030
• StarCoder2-15B-Instruct scored 72.6% on HumanEval benchmarks, surpassing CodeLlama-70B-Instruct despite having four times fewer parameters
• BigCode community grew to 1,100+ members with 49 derivative models built on StarCoder2-15B as of 2025
• The Stack v2 training dataset contains 67.5 TB of raw data covering 619 programming languages, representing a 10x increase over the original dataset

StarCoder Download and Adoption Metrics

The StarCoder model family shows distinct adoption patterns across different parameter configurations, with smaller models gaining significantly more traction among developers.

StarCoder2-3B leads adoption with 163,000 monthly downloads, followed by StarCoder2-7B at 24,300 downloads and StarCoder2-15B at 5,335 downloads. The 3B variant accounts for 84.7% of total downloads across all variants.

The smaller model’s dominance reflects demand for efficient solutions that run on consumer hardware without requiring enterprise-grade infrastructure. Developers can deploy the 3B variant on standard laptops and workstations, making it accessible for individual practitioners and small teams.

Adoption Metric	Value
BigCode Community Members	1,100+
BigCode Hugging Face Followers	1,780
StarCoder2-15B Likes	649
Hugging Face Spaces Using StarCoder2-15B	86+
Total Derivative Models	49

StarCoder Market Position and Growth

StarCoder operates within an expanding AI coding tools sector that transformed developer workflows across the software industry. The market reached $7.37 billion in 2025 and projects to grow to $23.97 billion by 2030.

The 26.60% compound annual growth rate positions AI coding assistance as one of the fastest-growing segments in developer tools. AI development tools continue advancing as 63% of professional developers now use AI in their development processes.

GitHub Copilot reached 15 million users by 2025, while 90% of Fortune 100 companies adopted AI coding tools. StarCoder provides an open-source alternative for organizations requiring data sovereignty and on-premises deployment capabilities.

Market Metric	2025 Value
AI Code Tools Market Size	$7.37 billion
Projected Market Size (2030)	$23.97 billion
Market CAGR (2025-2030)	26.60%
GitHub Copilot Users	15+ million
Fortune 100 AI Coding Adoption	90%
Developers Using AI	63%

StarCoder Model Architecture and Parameters

The StarCoder family comprises multiple configurations designed for different computational requirements and use cases. Each variant balances performance with resource efficiency.

StarCoder2-15B represents the flagship model with 15 billion parameters trained on 4+ trillion tokens. The model uses Grouped Query Attention with a sliding window mechanism of 4,096 tokens and a context window of 16,384 tokens.

Training occurred on the NVIDIA Eos Supercomputer, built with 1,024 NVIDIA DGX H100 systems. The model completed 1 million pretraining steps using bfloat16 precision. All StarCoder2 variants employ Fill-in-the-Middle training, enabling code completion by understanding both preceding and following context.

Model Variant	Parameters	Training Tokens	Context Window	Languages
StarCoder (Original)	15.5 billion	1 trillion	8,192 tokens	80+
StarCoder2-3B	3 billion	3.3+ trillion	16,384 tokens	17
StarCoder2-7B	7 billion	3.5+ trillion	16,384 tokens	17
StarCoder2-15B	15 billion	4+ trillion	16,384 tokens	600+

Hardware Requirements and Memory Footprint

Memory requirements vary significantly based on precision configuration. Full precision deployment of StarCoder2-15B requires 32,251 MB, while 4-bit quantization reduces this to 9,225 MB.

The 4-bit quantized version achieves a 71% reduction in memory requirements compared to full precision, enabling deployment on consumer GPUs with 12GB VRAM. GGUF Q2_K variants require under 7GB, making local inference feasible on laptops without dedicated AI accelerators.

Precision Mode	Memory Footprint
Full Precision (bfloat16)	32,251 MB
8-bit Quantization	16,900 MB
4-bit Quantization	9,225 MB
GGUF Q8_0	17,000 MB
GGUF Q4_K_M	9,860 MB
GGUF Q2_K	6,190 MB

StarCoder Training Dataset Composition

The Stack v2 serves as the training foundation for StarCoder2 models, representing the largest open code dataset suitable for large language model pretraining. The dataset was developed in partnership with Software Heritage, the largest public archive of software source code.

The Stack v2 contains 67.5 TB of raw data, delivering a training set 10x larger than its predecessor. The dataset includes 900+ billion unique training tokens across 619 programming languages, compared to 200 billion tokens and 86 languages in The Stack v1.

Dataset Metric	The Stack v1	The Stack v2
Raw Dataset Size	6.4 TB	67.5 TB
Unique Training Tokens	~200 billion	900+ billion
Programming Languages	86	619
GitHub Issues	54 GB	Included + expanded
Jupyter Notebooks	13 GB	Kaggle notebooks added

Additional high-quality sources incorporated into training include Wikipedia technical articles, ArXiv papers, and code documentation. The BigCode team implemented comprehensive data preparation including deduplication, malicious code removal, decontamination, and developer opt-out processing.

StarCoder Benchmark Performance Results

StarCoder2 models demonstrated competitive performance across industry-standard benchmarks measuring code generation, completion, and reasoning capabilities. The BigCodeBench framework, presented at ICLR 2025, includes 1,140 programming tasks assessing tool use and complex instruction following.

StarCoder2-15B-Instruct achieved a 72.6% score on HumanEval, surpassing CodeLlama-70B-Instruct’s 72.0% despite having four times fewer parameters. This marks the first fully transparent and permissively licensed code model to exceed 70% on HumanEval.

The 3B model matches the performance of the original 15.5B StarCoder on key benchmarks, demonstrating significant efficiency improvements through enhanced training data and techniques. AI-powered code completion continues evolving as models achieve higher accuracy with smaller parameter counts.

Benchmark	StarCoder (Original)	StarCoder2-15B	StarCoder2-15B-Instruct
HumanEval (pass@1)	29.3%	46.3%	72.6%
HumanEval+ (pass@1)	N/A	37.8%	N/A
MBPP	N/A	N/A	75.2%
DS-1000	N/A	33.8%	40.6%
GSM8K (PAL)	N/A	65.1%	N/A

StarCoder Derivative Models and Ecosystem

The open-source nature of StarCoder spawned an extensive ecosystem of derivative models, demonstrating community engagement with the platform. StarCoder2-15B generated 49 derivative models across multiple categories.

The ecosystem includes 20 fine-tuned models, 18 quantized versions, and 11 adapter models. Quantized versions enable deployment across varying hardware configurations, with GGUF formats supporting local inference on consumer GPUs.

The model ecosystem continues expanding as researchers and enterprises customize StarCoder2 for specialized domains including enterprise code modernization, security scanning, and domain-specific programming languages. The BigCode OpenRAIL-M v1 license ensures royalty-free distribution while simplifying commercial integration.

StarCoder Environmental Impact Data

The BigCode team maintained transparency regarding the environmental footprint of training StarCoder models, establishing accountability standards for responsible AI development.

Training the original StarCoderBase required 320,256 total GPU hours and consumed 89,672 kWh of electricity. The base model training produced 16.68 tonnes of CO2 equivalent emissions, with fine-tuning adding 0.58 tonnes.

Environmental Metric	StarCoderBase
Total GPU Hours	320,256 hours
Electricity Consumed	89,672 kWh
Carbon Emissions (Base)	16.68 tonnes CO2eq
Fine-tuning Emissions	0.58 tonnes CO2eq

This environmental transparency enables organizations to make informed decisions about computational sustainability when selecting code generation models. AI energy consumption remains a consideration as training demands increase across the industry.

FAQ

How many monthly downloads does StarCoder2-3B have?

StarCoder2-3B recorded 163,000 monthly downloads as of 2025, making it the most downloaded variant. This represents 84.7% of total downloads across all StarCoder2 model configurations.

What is the projected AI coding tools market size by 2030?

The AI coding tools market projects to reach $23.97 billion by 2030, growing from $7.37 billion in 2025 at a compound annual growth rate of 26.60%.

What HumanEval score did StarCoder2-15B-Instruct achieve?

StarCoder2-15B-Instruct achieved a 72.6% score on HumanEval benchmarks, marking the first fully transparent and permissively licensed code model to exceed 70% on this standard evaluation.

How many programming languages does The Stack v2 dataset cover?

The Stack v2 dataset covers 619 programming languages across 67.5 TB of raw data, representing a significant expansion from the 86 languages in The Stack v1.

What is the minimum GPU memory required to run StarCoder2-15B?

StarCoder2-15B requires approximately 6,190 MB with GGUF Q2_K quantization, making it deployable on consumer GPUs with 8GB VRAM. Full precision deployment requires 32,251 MB.